{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.\n",
      "__main__"
     ]
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     "text": [
      "03/17/2019 16:30:43 - INFO - __main__ -   device: cuda n_gpu: 4, distributed training: False, 16-bits training: False\n",
      "03/17/2019 16:30:43 - INFO - pytorch_pretrained_bert.tokenization -   loading vocabulary file https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt from cache at /home/belgundlibharat/.pytorch_pretrained_bert/9b3c03a36e83b13d5ba95ac965c9f9074a99e14340c523ab405703179e79fc46.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n",
      "03/17/2019 16:30:44 - INFO - pytorch_pretrained_bert.modeling -   loading archive file https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz from cache at /home/belgundlibharat/.pytorch_pretrained_bert/distributed_-1/214d4777e8e3eb234563136cd3a49f6bc34131de836848454373fa43f10adc5e.abfbb80ee795a608acbf35c7bf2d2d58574df3887cdd94b355fc67e03fddba05\n",
      "03/17/2019 16:30:44 - INFO - pytorch_pretrained_bert.modeling -   extracting archive file /home/belgundlibharat/.pytorch_pretrained_bert/distributed_-1/214d4777e8e3eb234563136cd3a49f6bc34131de836848454373fa43f10adc5e.abfbb80ee795a608acbf35c7bf2d2d58574df3887cdd94b355fc67e03fddba05 to temp dir /tmp/tmp5nw2vn6l\n",
      "03/17/2019 16:30:55 - INFO - pytorch_pretrained_bert.modeling -   Model config {\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 1024,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 4096,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"num_attention_heads\": 16,\n",
      "  \"num_hidden_layers\": 24,\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "03/17/2019 16:31:07 - INFO - pytorch_pretrained_bert.modeling -   Weights of BertForQuestionAnswering not initialized from pretrained model: ['qa_outputs.weight', 'qa_outputs.bias']\n",
      "03/17/2019 16:31:07 - INFO - pytorch_pretrained_bert.modeling -   Weights from pretrained model not used in BertForQuestionAnswering: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000000\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] is project on track ? [SEP] feature planning is done . 5 features are taken for current sprint . video file par ##sing , deco ##mp ##ress ##ion , extract ##ing video characteristics , there are 3 members with experience . 3 fresh members . initial study is in progress . a prototype demo of extract ##ing video features planned in current sprint . for the next sprint , there is dependency on new hardware . the procurement is initiated . we need management support in speeding it up . overall , we are on schedule . there is no risk for current sprint . but for next sprint we need the hardware . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 7:0 8:1 9:2 10:3 11:3 12:4 13:5 14:6 15:7 16:8 17:9 18:10 19:10 20:11 21:12 22:13 23:13 24:13 25:14 26:14 27:14 28:14 29:14 30:15 31:15 32:16 33:17 34:17 35:18 36:19 37:20 38:21 39:22 40:23 41:23 42:24 43:25 44:26 45:26 46:27 47:28 48:29 49:30 50:31 51:31 52:32 53:33 54:34 55:35 56:36 57:36 58:37 59:38 60:39 61:40 62:41 63:42 64:42 65:43 66:44 67:45 68:46 69:46 70:47 71:48 72:49 73:50 74:51 75:52 76:52 77:53 78:54 79:55 80:56 81:56 82:57 83:58 84:59 85:60 86:61 87:62 88:63 89:64 90:64 91:64 92:64 93:65 94:66 95:67 96:68 97:68 98:69 99:70 100:71 101:72 102:73 103:74 104:75 105:75 106:76 107:77 108:78 109:79 110:80 111:81 112:82 113:83 114:83\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 7:True 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True 95:True 96:True 97:True 98:True 99:True 100:True 101:True 102:True 103:True 104:True 105:True 106:True 107:True 108:True 109:True 110:True 111:True 112:True 113:True 114:True\n",
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000001\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 1\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] any risks ? [SEP] feature planning is done . 5 features are taken for current sprint . video file par ##sing , deco ##mp ##ress ##ion , extract ##ing video characteristics , there are 3 members with experience . 3 fresh members . initial study is in progress . a prototype demo of extract ##ing video features planned in current sprint . for the next sprint , there is dependency on new hardware . the procurement is initiated . we need management support in speeding it up . overall , we are on schedule . there is no risk for current sprint . but for next sprint we need the hardware . [SEP]\n"
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000002\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 2\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] any support required ? [SEP] feature planning is done . 5 features are taken for current sprint . video file par ##sing , deco ##mp ##ress ##ion , extract ##ing video characteristics , there are 3 members with experience . 3 fresh members . initial study is in progress . a prototype demo of extract ##ing video features planned in current sprint . for the next sprint , there is dependency on new hardware . the procurement is initiated . we need management support in speeding it up . overall , we are on schedule . there is no risk for current sprint . but for next sprint we need the hardware . [SEP]\n",
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000003\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 3\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] is project on track ? [SEP] implementation is ha ##rl ##f ##way few features implemented , demonstrated and called ' done ' . after that found that few features are to be reworked based on additional technical understanding team got . for this , few additional stories had to be created . 1 experienced member has quit . interview for recruitment is in progress . overall , project is 3 weeks behind schedule with respect to the release plan . by the end of next sprint , it will get back on schedule . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 7:0 8:1 9:2 10:2 11:2 12:2 13:3 14:4 15:5 16:5 17:6 18:7 19:8 20:9 21:9 22:9 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:18 33:19 34:20 35:21 36:22 37:23 38:24 39:25 40:26 41:26 42:27 43:28 44:28 45:29 46:30 47:31 48:32 49:33 50:34 51:35 52:35 53:36 54:37 55:38 56:39 57:40 58:40 59:41 60:42 61:43 62:44 63:45 64:46 65:46 66:47 67:47 68:48 69:49 70:50 71:51 72:52 73:53 74:54 75:55 76:56 77:57 78:58 79:59 80:59 81:60 82:61 83:62 84:63 85:64 86:65 87:65 88:66 89:67 90:68 91:69 92:70 93:71 94:71\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 7:True 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2003 2622 2006 2650 1029 102 7375 2003 5292 12190 2546 4576 2261 2838 7528 1010 7645 1998 2170 1005 2589 1005 1012 2044 2008 2179 2008 2261 2838 2024 2000 2022 27575 2241 2006 3176 4087 4824 2136 2288 1012 2005 2023 1010 2261 3176 3441 2018 2000 2022 2580 1012 1015 5281 2266 2038 8046 1012 4357 2005 15680 2003 1999 5082 1012 3452 1010 2622 2003 1017 3134 2369 6134 2007 4847 2000 1996 2713 2933 1012 2011 1996 2203 1997 2279 9043 1010 2009 2097 2131 2067 2006 6134 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000004\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 4\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] is there project risk ? [SEP] implementation is ha ##rl ##f ##way few features implemented , demonstrated and called ' done ' . after that found that few features are to be reworked based on additional technical understanding team got . for this , few additional stories had to be created . 1 experienced member has quit . interview for recruitment is in progress . overall , project is 3 weeks behind schedule with respect to the release plan . by the end of next sprint , it will get back on schedule . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 7:0 8:1 9:2 10:2 11:2 12:2 13:3 14:4 15:5 16:5 17:6 18:7 19:8 20:9 21:9 22:9 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:18 33:19 34:20 35:21 36:22 37:23 38:24 39:25 40:26 41:26 42:27 43:28 44:28 45:29 46:30 47:31 48:32 49:33 50:34 51:35 52:35 53:36 54:37 55:38 56:39 57:40 58:40 59:41 60:42 61:43 62:44 63:45 64:46 65:46 66:47 67:47 68:48 69:49 70:50 71:51 72:52 73:53 74:54 75:55 76:56 77:57 78:58 79:59 80:59 81:60 82:61 83:62 84:63 85:64 86:65 87:65 88:66 89:67 90:68 91:69 92:70 93:71 94:71\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 7:True 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2003 2045 2622 3891 1029 102 7375 2003 5292 12190 2546 4576 2261 2838 7528 1010 7645 1998 2170 1005 2589 1005 1012 2044 2008 2179 2008 2261 2838 2024 2000 2022 27575 2241 2006 3176 4087 4824 2136 2288 1012 2005 2023 1010 2261 3176 3441 2018 2000 2022 2580 1012 1015 5281 2266 2038 8046 1012 4357 2005 15680 2003 1999 5082 1012 3452 1010 2622 2003 1017 3134 2369 6134 2007 4847 2000 1996 2713 2933 1012 2011 1996 2203 1997 2279 9043 1010 2009 2097 2131 2067 2006 6134 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000005\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 5\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] how is the team ? [SEP] implementation is ha ##rl ##f ##way few features implemented , demonstrated and called ' done ' . after that found that few features are to be reworked based on additional technical understanding team got . for this , few additional stories had to be created . 1 experienced member has quit . interview for recruitment is in progress . overall , project is 3 weeks behind schedule with respect to the release plan . by the end of next sprint , it will get back on schedule . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 7:0 8:1 9:2 10:2 11:2 12:2 13:3 14:4 15:5 16:5 17:6 18:7 19:8 20:9 21:9 22:9 23:9 24:10 25:11 26:12 27:13 28:14 29:15 30:16 31:17 32:18 33:19 34:20 35:21 36:22 37:23 38:24 39:25 40:26 41:26 42:27 43:28 44:28 45:29 46:30 47:31 48:32 49:33 50:34 51:35 52:35 53:36 54:37 55:38 56:39 57:40 58:40 59:41 60:42 61:43 62:44 63:45 64:46 65:46 66:47 67:47 68:48 69:49 70:50 71:51 72:52 73:53 74:54 75:55 76:56 77:57 78:58 79:59 80:59 81:60 82:61 83:62 84:63 85:64 86:65 87:65 88:66 89:67 90:68 91:69 92:70 93:71 94:71\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 7:True 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True 92:True 93:True 94:True\n",
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      "03/17/2019 16:31:51 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000006\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 6\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] is there any customer feedback ? [SEP] 6 features were del ##ie ##vre ##d . customer has tested . there is one change request from product management . 1 defect reported from customer . also due to wrong merging , last sprint ' s features were affected . these defects and cleanup ##s will require this sprint . feature change request will be taken up in next sprint . overall there is slip ##page of 2 weeks . project status is yellow delayed but management involvement not necessary . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 8:0 9:1 10:2 11:3 12:3 13:3 14:3 15:3 16:4 17:5 18:6 19:6 20:7 21:8 22:9 23:10 24:11 25:12 26:13 27:14 28:14 29:15 30:16 31:17 32:18 33:19 34:19 35:20 36:21 37:22 38:23 39:24 40:24 41:25 42:26 43:26 44:26 45:27 46:28 47:29 48:29 49:30 50:31 51:32 52:33 53:33 54:34 55:35 56:36 57:37 58:37 59:38 60:39 61:40 62:41 63:42 64:43 65:44 66:45 67:46 68:47 69:47 70:48 71:49 72:50 73:51 74:51 75:52 76:53 77:54 78:54 79:55 80:56 81:57 82:58 83:59 84:60 85:61 86:62 87:63 88:64 89:64\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True\n"
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      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2003 2045 2151 8013 12247 1029 102 1020 2838 2020 3972 2666 12229 2094 1012 8013 2038 7718 1012 2045 2003 2028 2689 5227 2013 4031 2968 1012 1015 21262 2988 2013 8013 1012 2036 2349 2000 3308 16468 1010 2197 9043 1005 1055 2838 2020 5360 1012 2122 18419 1998 27686 2015 2097 5478 2023 9043 1012 3444 2689 5227 2097 2022 2579 2039 1999 2279 9043 1012 3452 2045 2003 7540 13704 1997 1016 3134 1012 2622 3570 2003 3756 8394 2021 2968 6624 2025 4072 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000007\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 7\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] how are we doing with project schedule ? [SEP] 6 features were del ##ie ##vre ##d . customer has tested . there is one change request from product management . 1 defect reported from customer . also due to wrong merging , last sprint ' s features were affected . these defects and cleanup ##s will require this sprint . feature change request will be taken up in next sprint . overall there is slip ##page of 2 weeks . project status is yellow delayed but management involvement not necessary . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 10:0 11:1 12:2 13:3 14:3 15:3 16:3 17:3 18:4 19:5 20:6 21:6 22:7 23:8 24:9 25:10 26:11 27:12 28:13 29:14 30:14 31:15 32:16 33:17 34:18 35:19 36:19 37:20 38:21 39:22 40:23 41:24 42:24 43:25 44:26 45:26 46:26 47:27 48:28 49:29 50:29 51:30 52:31 53:32 54:33 55:33 56:34 57:35 58:36 59:37 60:37 61:38 62:39 63:40 64:41 65:42 66:43 67:44 68:45 69:46 70:47 71:47 72:48 73:49 74:50 75:51 76:51 77:52 78:53 79:54 80:54 81:55 82:56 83:57 84:58 85:59 86:60 87:61 88:62 89:63 90:64 91:64\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True 91:True\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2129 2024 2057 2725 2007 2622 6134 1029 102 1020 2838 2020 3972 2666 12229 2094 1012 8013 2038 7718 1012 2045 2003 2028 2689 5227 2013 4031 2968 1012 1015 21262 2988 2013 8013 1012 2036 2349 2000 3308 16468 1010 2197 9043 1005 1055 2838 2020 5360 1012 2122 18419 1998 27686 2015 2097 5478 2023 9043 1012 3444 2689 5227 2097 2022 2579 2039 1999 2279 9043 1012 3452 2045 2003 7540 13704 1997 1016 3134 1012 2622 3570 2003 3756 8394 2021 2968 6624 2025 4072 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   segment_ids: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000008\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 8\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] any lessons learnt ? [SEP] 6 features were del ##ie ##vre ##d . customer has tested . there is one change request from product management . 1 defect reported from customer . also due to wrong merging , last sprint ' s features were affected . these defects and cleanup ##s will require this sprint . feature change request will be taken up in next sprint . overall there is slip ##page of 2 weeks . project status is yellow delayed but management involvement not necessary . [SEP]\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 6:0 7:1 8:2 9:3 10:3 11:3 12:3 13:3 14:4 15:5 16:6 17:6 18:7 19:8 20:9 21:10 22:11 23:12 24:13 25:14 26:14 27:15 28:16 29:17 30:18 31:19 32:19 33:20 34:21 35:22 36:23 37:24 38:24 39:25 40:26 41:26 42:26 43:27 44:28 45:29 46:29 47:30 48:31 49:32 50:33 51:33 52:34 53:35 54:36 55:37 56:37 57:38 58:39 59:40 60:41 61:42 62:43 63:44 64:45 65:46 66:47 67:47 68:48 69:49 70:50 71:51 72:51 73:52 74:53 75:54 76:54 77:55 78:56 79:57 80:58 81:59 82:60 83:61 84:62 85:63 86:64 87:64\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 6:True 7:True 8:True 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2151 8220 20215 1029 102 1020 2838 2020 3972 2666 12229 2094 1012 8013 2038 7718 1012 2045 2003 2028 2689 5227 2013 4031 2968 1012 1015 21262 2988 2013 8013 1012 2036 2349 2000 3308 16468 1010 2197 9043 1005 1055 2838 2020 5360 1012 2122 18419 1998 27686 2015 2097 5478 2023 9043 1012 3444 2689 5227 2097 2022 2579 2039 1999 2279 9043 1012 3452 2045 2003 7540 13704 1997 1016 3134 1012 2622 3570 2003 3756 8394 2021 2968 6624 2025 4072 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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      "03/17/2019 16:31:51 - INFO - __main__ -   *** Example ***\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   unique_id: 1000000009\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   example_index: 9\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   doc_span_index: 0\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   tokens: [CLS] do you need any management support ? [SEP] 6 features were del ##ie ##vre ##d . customer has tested . there is one change request from product management . 1 defect reported from customer . also due to wrong merging , last sprint ' s features were affected . these defects and cleanup ##s will require this sprint . feature change request will be taken up in next sprint . overall there is slip ##page of 2 weeks . project status is yellow delayed but management involvement not necessary . [SEP]\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_to_orig_map: 9:0 10:1 11:2 12:3 13:3 14:3 15:3 16:3 17:4 18:5 19:6 20:6 21:7 22:8 23:9 24:10 25:11 26:12 27:13 28:14 29:14 30:15 31:16 32:17 33:18 34:19 35:19 36:20 37:21 38:22 39:23 40:24 41:24 42:25 43:26 44:26 45:26 46:27 47:28 48:29 49:29 50:30 51:31 52:32 53:33 54:33 55:34 56:35 57:36 58:37 59:37 60:38 61:39 62:40 63:41 64:42 65:43 66:44 67:45 68:46 69:47 70:47 71:48 72:49 73:50 74:51 75:51 76:52 77:53 78:54 79:54 80:55 81:56 82:57 83:58 84:59 85:60 86:61 87:62 88:63 89:64 90:64\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   token_is_max_context: 9:True 10:True 11:True 12:True 13:True 14:True 15:True 16:True 17:True 18:True 19:True 20:True 21:True 22:True 23:True 24:True 25:True 26:True 27:True 28:True 29:True 30:True 31:True 32:True 33:True 34:True 35:True 36:True 37:True 38:True 39:True 40:True 41:True 42:True 43:True 44:True 45:True 46:True 47:True 48:True 49:True 50:True 51:True 52:True 53:True 54:True 55:True 56:True 57:True 58:True 59:True 60:True 61:True 62:True 63:True 64:True 65:True 66:True 67:True 68:True 69:True 70:True 71:True 72:True 73:True 74:True 75:True 76:True 77:True 78:True 79:True 80:True 81:True 82:True 83:True 84:True 85:True 86:True 87:True 88:True 89:True 90:True\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   input_ids: 101 2079 2017 2342 2151 2968 2490 1029 102 1020 2838 2020 3972 2666 12229 2094 1012 8013 2038 7718 1012 2045 2003 2028 2689 5227 2013 4031 2968 1012 1015 21262 2988 2013 8013 1012 2036 2349 2000 3308 16468 1010 2197 9043 1005 1055 2838 2020 5360 1012 2122 18419 1998 27686 2015 2097 5478 2023 9043 1012 3444 2689 5227 2097 2022 2579 2039 1999 2279 9043 1012 3452 2045 2003 7540 13704 1997 1016 3134 1012 2622 3570 2003 3756 8394 2021 2968 6624 2025 4072 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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     "output_type": "stream",
     "text": [
      "03/17/2019 16:31:51 - INFO - __main__ -   ***** Running predictions *****\n",
      "03/17/2019 16:31:51 - INFO - __main__ -     Num orig examples = 10\n",
      "03/17/2019 16:31:51 - INFO - __main__ -     Num split examples = 10\n",
      "03/17/2019 16:31:51 - INFO - __main__ -     Batch size = 8\n",
      "03/17/2019 16:31:51 - INFO - __main__ -   Start evaluating\n",
      "Evaluating:   0%|          | 0/2 [00:00<?, ?it/s]03/17/2019 16:31:52 - INFO - __main__ -   Processing example: 0\n",
      "Evaluating:  50%|█████     | 1/2 [00:01<00:01,  1.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "This program is being run by itself\n",
      "tensor([[-10.7666, -10.1543,  -8.7376,  ..., -13.4492, -13.4143, -13.4186],\n",
      "        [-10.9756,  -9.8389,  -9.2742,  ..., -13.0148, -12.9941, -12.9916],\n",
      "        [-10.7076, -10.0442, -10.2649,  ..., -13.3072, -13.2767, -13.2176],\n",
      "        ...,\n",
      "        [-12.0089, -10.5963, -11.6384,  ..., -13.7870, -13.8336, -13.7907],\n",
      "        [-10.8961, -10.3473, -10.0462,  ..., -13.3651, -13.3726, -13.3904],\n",
      "        [-10.8798, -11.0198, -11.4544,  ..., -13.5760, -13.6187, -13.6717]],\n",
      "       device='cuda:0')\n",
      "tensor([[ -9.6859,  -9.5812,  -9.4685,  ..., -12.4528, -12.4033, -12.4175],\n",
      "        [-10.1506,  -9.2528,  -7.5280,  ..., -11.9704, -11.9298, -11.9168],\n",
      "        [ -9.9225,  -9.6589,  -9.5322,  ..., -12.4762, -12.4043, -12.3035],\n",
      "        ...,\n",
      "        [-10.7309,  -9.4070, -10.3608,  ..., -13.5432, -13.6157, -13.5164],\n",
      "        [-10.2544,  -9.3870,  -9.6832,  ..., -12.7517, -12.7708, -12.7964],\n",
      "        [ -9.8342,  -9.4285,  -9.8119,  ..., -12.8504, -12.8969, -12.9594]],\n",
      "       device='cuda:0')\n",
      "Printing all result\n",
      "[RawResult(unique_id=1000000000, start_logits=[-10.766557693481445, -10.154342651367188, -8.737625122070312, -8.509843826293945, -6.700699329376221, -12.757169723510742, -7.4241156578063965, 2.257307767868042, -0.9719355702400208, -1.4512035846710205, -2.9525058269500732, -3.3750338554382324, -0.13886141777038574, -3.4749183654785156, -4.301324367523193, -5.068531513214111, -4.098912715911865, -0.10428652167320251, -2.8028786182403564, -5.195619583129883, -1.1256765127182007, -4.984860897064209, -5.325179576873779, -5.536526679992676, -5.536569595336914, -4.924918174743652, -5.09438943862915, -5.953338623046875, -6.211363315582275, -6.034547805786133, -5.557287693023682, -6.129354476928711, -4.891765594482422, -6.124647617340088, -6.202759265899658, -2.2128477096557617, -5.050237655639648, -2.135871410369873, -5.268340110778809, -5.353346824645996, -5.660350799560547, -6.567471027374268, -0.9635396003723145, -4.469319820404053, -6.401462554931641, -3.727494716644287, 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      "Printing all result\n",
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      "Printing all result\n",
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      "Printing all result\n",
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      "Printing all result\n",
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      "Printing all result\n",
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      "Printing all result\n",
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      "Printing all result\n",
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      "Evaluating: 100%|██████████| 2/2 [00:01<00:00,  1.21s/it]\n",
      "03/17/2019 16:31:53 - INFO - __main__ -   Writing predictions to: //home//belgundlibharat//Output/predictions.json\n",
      "03/17/2019 16:31:53 - INFO - __main__ -   Writing nbest to: //home//belgundlibharat//Output/nbest_predictions.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       device='cuda:0')\n",
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      "         -12.4555, -12.5012, -12.4303, -12.3893, -12.3709, -12.4575, -12.3561,\n",
      "         -12.3873, -12.4471, -12.3783, -12.3573, -12.3689, -12.3933, -12.3717,\n",
      "         -12.4579, -12.4596, -12.4585, -12.4839, -12.4805, -12.4109, -12.4021,\n",
      "         -12.6264, -12.5255, -12.4262, -12.4339, -12.3973, -12.3860, -12.3733,\n",
      "         -12.5378, -12.3919, -12.3958, -12.4064, -12.3334, -12.3611, -12.3278,\n",
      "         -12.3749, -12.3781, -12.3564, -12.5419, -12.4136, -12.5062],\n",
      "        [-10.7055,  -9.3455, -10.3496,  -8.4090,  -8.5592,  -9.8289,  -8.0887,\n",
      "         -11.2133,  -8.0550,  -5.6345,  -6.5551,  -7.0715,  -6.9559,  -5.7812,\n",
      "          -6.3803,  -5.7448,  -5.8803,  -6.0808,  -6.8139,  -5.1777,  -5.3084,\n",
      "          -4.4773,  -5.8197,  -0.8519,  -4.1974,  -0.8034,  -6.4829,  -3.5092,\n",
      "          -0.8412,  -2.7956,  -2.4904,  -4.1690,  -4.3547,  -7.5205,  -2.9362,\n",
      "          -3.8743,  -5.6384,  -6.4650,  -6.4140,  -6.1851,  -5.1466,  -6.3467,\n",
      "          -7.0519,  -6.1050,  -9.7212,  -6.8119,  -6.3927,  -6.9956,  -4.7423,\n",
      "          -4.9978,  -7.1563,  -5.8473,  -6.7121,  -7.2638,  -5.4808,  -5.8060,\n",
      "          -5.4220,  -6.6441,  -3.4060,  -4.9259,  -4.5350,  -5.1380,  -3.2760,\n",
      "          -5.8148,  -6.1566,  -5.8229,  -4.9691,  -5.9529,  -6.4791,  -3.5140,\n",
      "          -3.5639,  -5.1160,  -6.1505,  -5.4850,  -5.6989,  -5.5523,  -6.9052,\n",
      "          -5.7797,  -2.9030,  -3.3973,  -3.5396,  -3.4995,  -5.1953,  -3.0641,\n",
      "          -1.1234,  -2.9045,  -2.2694,   3.1266,   1.3446,   4.9221,   3.1616,\n",
      "          -7.5704, -12.2687, -12.5076, -12.4900, -12.4168, -12.4887, -12.4177,\n",
      "         -12.5007, -12.6382, -12.5741, -12.4932, -12.4724, -12.3525, -12.2211,\n",
      "         -12.3789, -12.2889, -12.4493, -12.4757, -12.4440, -12.3468, -12.1551,\n",
      "         -12.2027, -12.1297, -12.0735, -12.1764, -12.2024, -12.1109, -12.1842,\n",
      "         -12.1824, -12.3156, -12.3420, -12.5660, -12.5158, -12.4266, -12.4857,\n",
      "         -12.3003, -12.5595, -12.5842, -12.5530, -12.5721, -12.5193, -12.4440,\n",
      "         -12.3341, -12.2890, -12.4619, -12.4182, -12.5107, -12.5619, -12.4538,\n",
      "         -12.5485, -12.3912, -12.3058, -12.5628, -12.3737, -12.3789, -12.3208,\n",
      "         -12.2624, -12.2504, -12.2795, -12.5332, -12.4849, -12.5367, -12.5507,\n",
      "         -12.4858, -12.5012, -12.5783, -12.4068, -12.6510, -12.6114, -12.5691,\n",
      "         -12.6124, -12.5402, -12.4913, -12.3767, -12.4942, -12.2946, -12.2692,\n",
      "         -12.5250, -12.5074, -12.4145, -12.5054, -12.5055, -12.2886, -12.2750,\n",
      "         -12.3472, -12.1387, -12.2163, -12.3086, -12.1957, -12.2473, -12.2103,\n",
      "         -12.3111, -12.3168, -12.5925, -12.5076, -12.4554, -12.4844, -12.4284,\n",
      "         -12.3073, -12.5783, -12.4957, -12.5452, -12.5251, -12.5230, -12.5199,\n",
      "         -12.3014, -12.5403, -12.2876, -12.3947, -12.4441, -12.4166, -12.3713,\n",
      "         -12.2906, -12.2714, -12.2439, -12.2589, -12.3893, -12.4750, -12.4793,\n",
      "         -12.3201, -12.3840, -12.3483, -12.2404, -12.2111, -12.4189, -12.4144,\n",
      "         -12.3155, -12.4214, -12.5115, -12.4473, -12.5099, -12.4554, -12.3497,\n",
      "         -12.3972, -12.5117, -12.6034, -12.5703, -12.4621, -12.4322, -12.3932,\n",
      "         -12.2725, -12.2810, -12.4950, -12.3776, -12.4495, -12.4997, -12.3943,\n",
      "         -12.3021, -12.1951, -12.2369, -12.2801, -12.1907, -12.2143, -12.2366,\n",
      "         -12.3786, -12.2689, -12.3023, -12.2361, -12.3434, -12.3069, -12.5245,\n",
      "         -12.5781, -12.5435, -12.5160, -12.5554, -12.4408, -12.3849, -12.5314,\n",
      "         -12.4908, -12.4069, -12.5178, -12.4256, -12.5377, -12.5296, -12.4760,\n",
      "         -12.3069, -12.5152, -12.4538, -12.3483, -12.3375, -12.3697, -12.2415,\n",
      "         -12.2244, -12.4896, -12.4078, -12.4482, -12.4968, -12.3437, -12.2294,\n",
      "         -12.3670, -12.1733, -12.2549, -12.2239, -12.3252, -12.2269, -12.2165,\n",
      "         -12.3423, -12.3423, -12.5689, -12.5521, -12.5085, -12.5794, -12.5161,\n",
      "         -12.4159, -12.4720, -12.5468, -12.3788, -12.4276, -12.5429, -12.5043,\n",
      "         -12.3734, -12.3096, -12.4075, -12.2300, -12.2042, -12.2534, -12.2621,\n",
      "         -12.2186, -12.1541, -12.1415, -12.1920, -12.3894, -12.3950, -12.3044,\n",
      "         -12.4808, -12.4733, -12.3931, -12.3508, -12.4530, -12.3127, -12.4105,\n",
      "         -12.5750, -12.4290, -12.3732, -12.2788, -12.3188, -12.2535, -12.2534,\n",
      "         -12.4804, -12.5005, -12.4682, -12.4206, -12.1980, -12.1694, -12.2477,\n",
      "         -12.1455, -12.2294, -12.5237, -12.4932, -12.4292, -12.5760, -12.5375,\n",
      "         -12.4082, -12.5584, -12.5483, -12.4989, -12.3554, -12.3570, -12.2055,\n",
      "         -12.1827, -12.3828, -12.2940, -12.3005, -12.4271, -12.1885, -12.1760,\n",
      "         -12.3806, -12.4229, -12.4434, -12.3940, -12.4708, -12.4422, -12.3010,\n",
      "         -12.4799, -12.5038, -12.4728, -12.5103, -12.4930, -12.5467, -12.3614,\n",
      "         -12.5020, -12.2357, -12.2454, -12.3674, -12.1634, -12.3217, -12.2157,\n",
      "         -12.1357, -12.0817, -12.1449, -12.1286, -12.2962, -12.2216]],\n",
      "       device='cuda:0')\n",
      "Printing all result\n",
      "[RawResult(unique_id=1000000000, start_logits=[-10.766557693481445, -10.154342651367188, -8.737625122070312, -8.509843826293945, -6.700699329376221, -12.757169723510742, -7.4241156578063965, 2.257307767868042, -0.9719355702400208, -1.4512035846710205, -2.9525058269500732, -3.3750338554382324, -0.13886141777038574, -3.4749183654785156, -4.301324367523193, -5.068531513214111, -4.098912715911865, -0.10428652167320251, -2.8028786182403564, -5.195619583129883, -1.1256765127182007, -4.984860897064209, -5.325179576873779, -5.536526679992676, -5.536569595336914, -4.924918174743652, -5.09438943862915, -5.953338623046875, -6.211363315582275, -6.034547805786133, -5.557287693023682, -6.129354476928711, -4.891765594482422, -6.124647617340088, -6.202759265899658, -2.2128477096557617, -5.050237655639648, -2.135871410369873, -5.268340110778809, -5.353346824645996, -5.660350799560547, -6.567471027374268, -0.9635396003723145, -4.469319820404053, -6.401462554931641, -3.727494716644287, 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      "Printing all result\n",
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      "printing output text\n",
      "we are on schedule\n",
      "printing final text\n",
      "we are on schedule\n",
      "printing output text\n",
      "Overall, we are on schedule\n",
      "printing final text\n",
      "Overall, we are on schedule\n",
      "printing output text\n",
      "on schedule\n",
      "printing final text\n",
      "on schedule\n",
      "printing output text\n",
      "we are on schedule.\n",
      "printing final text\n",
      "we are on schedule.\n",
      "printing output text\n",
      "we are on schedule. There is no risk for current sprint\n",
      "printing final text\n",
      "we are on schedule. There is no risk for current sprint\n",
      "printing output text\n",
      "Overall, we are on schedule.\n",
      "printing final text\n",
      "Overall, we are on schedule.\n",
      "printing output text\n",
      "on schedule.\n",
      "printing final text\n",
      "on schedule.\n",
      "printing output text\n",
      "Initial study is in progress\n",
      "printing final text\n",
      "Initial study is in progress\n",
      "printing output text\n",
      "Overall, we are on schedule. There is no risk for current sprint\n",
      "printing final text\n",
      "Overall, we are on schedule. There is no risk for current sprint\n",
      "printing output text\n",
      "on schedule. There is no risk for current sprint\n",
      "printing final text\n",
      "on schedule. There is no risk for current sprint\n",
      "printing output text\n",
      "we are on schedule. There is no risk for current sprint.\n",
      "printing final text\n",
      "we are on schedule. There is no risk for current sprint.\n",
      "printing output text\n",
      "Initial study is in progress.\n",
      "printing final text\n",
      "Initial study is in progress.\n",
      "printing output text\n",
      "Initial study is in progress. A prototype demo of extracting video features planned in current sprint\n",
      "printing final text\n",
      "Initial study is in progress. A prototype demo of extracting video features planned in current sprint\n",
      "printing output text\n",
      "Initial study is in progress. A prototype demo of extracting video features planned in current sprint.\n",
      "printing final text\n",
      "Initial study is in progress. A prototype demo of extracting video features planned in current sprint.\n",
      "printing output text\n",
      "Overall, we are on schedule. There is no risk for current sprint.\n",
      "printing final text\n",
      "Overall, we are on schedule. There is no risk for current sprint.\n",
      "printing output text\n",
      "are on schedule\n",
      "printing final text\n",
      "are on schedule\n",
      "printing output text\n",
      "on schedule. There is no risk for current sprint.\n",
      "printing final text\n",
      "on schedule. There is no risk for current sprint.\n",
      "printing output text\n",
      "We need management support in speeding it up.Overall, we are on schedule\n",
      "printing final text\n",
      "We need management support in speeding it up.Overall, we are on schedule\n",
      "printing output text\n",
      "we are on schedule. There is no risk for current sprint. But for next sprint we need the hardware.\n",
      "printing final text\n",
      "we are on schedule. There is no risk for current sprint. But for next sprint we need the hardware.\n",
      "printing output text\n",
      ".Overall, we are on schedule\n",
      "printing final text\n",
      ".Overall, we are on schedule\n",
      "printing output text\n",
      "There is no risk\n",
      "printing final text\n",
      "There is no risk\n",
      "printing output text\n",
      "no risk\n",
      "printing final text\n",
      "no risk\n",
      "printing output text\n",
      "There is no risk for current sprint\n",
      "printing final text\n",
      "There is no risk for current sprint\n",
      "printing output text\n",
      "no risk for current sprint\n",
      "printing final text\n",
      "no risk for current sprint\n",
      "printing output text\n",
      "There is no risk for current sprint.\n",
      "printing final text\n",
      "There is no risk for current sprint.\n",
      "printing output text\n",
      "no risk for current sprint.\n",
      "printing final text\n",
      "no risk for current sprint.\n",
      "printing output text\n",
      "There is no\n",
      "printing final text\n",
      "There is no\n",
      "printing output text\n",
      "no\n",
      "printing final text\n",
      "no\n",
      "printing output text\n",
      "current sprint\n",
      "printing final text\n",
      "current sprint\n",
      "printing output text\n",
      "for current sprint\n",
      "printing final text\n",
      "for current sprint\n",
      "printing output text\n",
      ". There is no risk\n",
      "printing final text\n",
      ". There is no risk\n",
      "printing output text\n",
      "risk\n",
      "printing final text\n",
      "risk\n",
      "printing output text\n",
      "is no risk\n",
      "printing final text\n",
      "is no risk\n",
      "printing output text\n",
      "There is\n",
      "printing final text\n",
      "There is\n",
      "printing output text\n",
      "current sprint.\n",
      "printing final text\n",
      "current sprint.\n",
      "printing output text\n",
      ". There is no risk for current sprint\n",
      "printing final text\n",
      ". There is no risk for current sprint\n",
      "printing output text\n",
      "risk for current sprint\n",
      "printing final text\n",
      "risk for current sprint\n",
      "printing output text\n",
      "for current sprint.\n",
      "printing final text\n",
      "for current sprint.\n",
      "printing output text\n",
      "There is no risk for current sprint. But for next sprint we need the hardware.\n",
      "printing final text\n",
      "There is no risk for current sprint. But for next sprint we need the hardware.\n",
      "printing output text\n",
      "is no risk for current sprint\n",
      "printing final text\n",
      "is no risk for current sprint\n",
      "printing output text\n",
      "management support\n",
      "printing final text\n",
      "management support\n",
      "printing output text\n",
      "management support in speeding it up\n",
      "printing final text\n",
      "management support in speeding it up\n",
      "printing output text\n",
      "management\n",
      "printing final text\n",
      "management\n",
      "printing output text\n",
      "We need management support\n",
      "printing final text\n",
      "We need management support\n",
      "printing output text\n",
      "We need management support in speeding it up\n",
      "printing final text\n",
      "We need management support in speeding it up\n",
      "printing output text\n",
      "We need management\n",
      "printing final text\n",
      "We need management\n",
      "printing output text\n",
      "management support in speeding it up.\n",
      "printing final text\n",
      "management support in speeding it up.\n",
      "printing output text\n",
      "We need management support in speeding it up.\n",
      "printing final text\n",
      "We need management support in speeding it up.\n",
      "printing output text\n",
      "speeding it up\n",
      "printing final text\n",
      "speeding it up\n",
      "printing output text\n",
      "management support in speeding\n",
      "printing final text\n",
      "management support in speeding\n",
      "printing output text\n",
      "in speeding it up\n",
      "printing final text\n",
      "in speeding it up\n",
      "printing output text\n",
      "speeding it up.\n",
      "printing final text\n",
      "speeding it up.\n",
      "printing output text\n",
      "need management support\n",
      "printing final text\n",
      "need management support\n",
      "printing output text\n",
      "management support in speeding it up.Overall, we are on schedule. There is no risk for current sprint.\n",
      "printing final text\n",
      "management support in speeding it up.Overall, we are on schedule. There is no risk for current sprint.\n",
      "printing output text\n",
      "need management support in speeding it up\n",
      "printing final text\n",
      "need management support in speeding it up\n",
      "printing output text\n",
      "We need management support in speeding\n",
      "printing final text\n",
      "We need management support in speeding\n",
      "printing output text\n",
      "management support in speeding it up.Overall, we are on schedule\n",
      "printing final text\n",
      "management support in speeding it up.Overall, we are on schedule\n",
      "printing output text\n",
      "management support in\n",
      "printing final text\n",
      "management support in\n",
      "printing output text\n",
      "management support in speeding it up.Overall, we are on schedule. There is no risk for current sprint. But for next sprint we need the hardware\n",
      "printing final text\n",
      "management support in speeding it up.Overall, we are on schedule. There is no risk for current sprint. But for next sprint we need the hardware\n",
      "printing output text\n",
      "support\n",
      "printing final text\n",
      "support\n",
      "printing output text\n",
      "project is 3 weeks behind schedule\n",
      "printing final text\n",
      "project is 3 weeks behind schedule\n",
      "printing output text\n",
      "3 weeks behind schedule\n",
      "printing final text\n",
      "3 weeks behind schedule\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule\n",
      "printing output text\n",
      "By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "it will get back on schedule\n",
      "printing final text\n",
      "it will get back on schedule\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "it will get back on schedule.\n",
      "printing final text\n",
      "it will get back on schedule.\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "project is 3 weeks behind schedule\n",
      "printing final text\n",
      "project is 3 weeks behind schedule\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "3 weeks behind schedule\n",
      "printing final text\n",
      "3 weeks behind schedule\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing final text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule with respect to the release plan\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule\n",
      "printing output text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule\n",
      "printing final text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "it will get back on schedule.\n",
      "printing final text\n",
      "it will get back on schedule.\n",
      "printing output text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing final text\n",
      "3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule\n",
      "printing output text\n",
      "By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan. By the end of next sprint, it will get back on schedule.\n",
      "printing output text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing final text\n",
      "1 Experienced member has quit. Interview for recruitment is in progress. Overall, project is 3 weeks behind schedule with respect to the release plan.\n",
      "printing output text\n",
      "it will get back on schedule\n",
      "printing final text\n",
      "it will get back on schedule\n",
      "printing output text\n",
      "additional technical understanding\n",
      "printing final text\n",
      "additional technical understanding\n",
      "printing output text\n",
      "additional technical understanding team got\n",
      "printing final text\n",
      "additional technical understanding team got\n",
      "printing output text\n",
      "few features are to be reworked based on additional technical understanding\n",
      "printing final text\n",
      "few features are to be reworked based on additional technical understanding\n",
      "printing output text\n",
      "technical understanding\n",
      "printing final text\n",
      "technical understanding\n",
      "printing output text\n",
      "few features are to be reworked based on additional technical understanding team got\n",
      "printing final text\n",
      "few features are to be reworked based on additional technical understanding team got\n",
      "printing output text\n",
      "technical understanding team got\n",
      "printing final text\n",
      "technical understanding team got\n",
      "printing output text\n",
      "based on additional technical understanding\n",
      "printing final text\n",
      "based on additional technical understanding\n",
      "printing output text\n",
      "few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing final text\n",
      "few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing output text\n",
      "team got\n",
      "printing final text\n",
      "team got\n",
      "printing output text\n",
      "based on additional technical understanding team got\n",
      "printing final text\n",
      "based on additional technical understanding team got\n",
      "printing output text\n",
      "few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding team got\n",
      "printing final text\n",
      "few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding team got\n",
      "printing output text\n",
      "harlfway few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing final text\n",
      "harlfway few features implemented, demonstrated and called 'done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing output text\n",
      "additional technical understanding team got. For this, few additional stories had to be created\n",
      "printing final text\n",
      "additional technical understanding team got. For this, few additional stories had to be created\n",
      "printing output text\n",
      "found that few features are to be reworked based on additional technical understanding\n",
      "printing final text\n",
      "found that few features are to be reworked based on additional technical understanding\n",
      "printing output text\n",
      "done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing final text\n",
      "done'. After that found that few features are to be reworked based on additional technical understanding\n",
      "printing output text\n",
      "additional technical understanding team got. For this, few additional stories had to be created. 1 Experienced member has quit. Interview for recruitment\n",
      "printing final text\n",
      "additional technical understanding team got. For this, few additional stories had to be created. 1 Experienced member has quit. Interview for recruitment\n",
      "printing output text\n",
      "project is 3 weeks behind schedule\n",
      "printing final text\n",
      "project is 3 weeks behind schedule\n",
      "printing output text\n",
      "3 weeks behind schedule\n",
      "printing final text\n",
      "3 weeks behind schedule\n",
      "printing output text\n",
      "additional technical understanding team got. For this, few additional stories had to be created. 1 Experienced member has quit. Interview for recruitment is in progress\n",
      "printing final text\n",
      "additional technical understanding team got. For this, few additional stories had to be created. 1 Experienced member has quit. Interview for recruitment is in progress\n",
      "printing output text\n",
      "additional technical understanding team got.\n",
      "printing final text\n",
      "additional technical understanding team got.\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer\n",
      "printing output text\n",
      "Customer has tested\n",
      "printing final text\n",
      "Customer has tested\n",
      "printing output text\n",
      "There is one change request from product management. 1 Defect reported from customer\n",
      "printing final text\n",
      "There is one change request from product management. 1 Defect reported from customer\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management\n",
      "printing output text\n",
      "There is one change request from product management\n",
      "printing final text\n",
      "There is one change request from product management\n",
      "printing output text\n",
      "1 Defect reported from customer\n",
      "printing final text\n",
      "1 Defect reported from customer\n",
      "printing output text\n",
      "Customer has tested.\n",
      "printing final text\n",
      "Customer has tested.\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer.\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer.\n",
      "printing output text\n",
      "one change request from product management. 1 Defect reported from customer\n",
      "printing final text\n",
      "one change request from product management. 1 Defect reported from customer\n",
      "printing output text\n",
      "Customer has tested. There is one change request\n",
      "printing final text\n",
      "Customer has tested. There is one change request\n",
      "printing output text\n",
      "There is one change request from product management. 1 Defect reported from customer.\n",
      "printing final text\n",
      "There is one change request from product management. 1 Defect reported from customer.\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management.\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management.\n",
      "printing output text\n",
      "one change request from product management\n",
      "printing final text\n",
      "one change request from product management\n",
      "printing output text\n",
      "There is one change request\n",
      "printing final text\n",
      "There is one change request\n",
      "printing output text\n",
      "There is one change request from product management.\n",
      "printing final text\n",
      "There is one change request from product management.\n",
      "printing output text\n",
      "1 Defect reported from customer.\n",
      "printing final text\n",
      "1 Defect reported from customer.\n",
      "printing output text\n",
      "one change request from product management. 1 Defect reported from customer.\n",
      "printing final text\n",
      "one change request from product management. 1 Defect reported from customer.\n",
      "printing output text\n",
      "6 Features were delievred. Customer has tested. There is one change request from product management. 1 Defect reported from customer\n",
      "printing final text\n",
      "6 Features were delievred. Customer has tested. There is one change request from product management. 1 Defect reported from customer\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported\n",
      "printing output text\n",
      "change request from product management. 1 Defect reported from customer\n",
      "printing final text\n",
      "change request from product management. 1 Defect reported from customer\n",
      "printing output text\n",
      "Project status is Yellow Delayed\n",
      "printing final text\n",
      "Project status is Yellow Delayed\n",
      "printing output text\n",
      "Yellow Delayed\n",
      "printing final text\n",
      "Yellow Delayed\n",
      "printing output text\n",
      "Project status is Yellow Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Project status is Yellow Delayed but management involvement not necessary\n",
      "printing output text\n",
      "Yellow Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Yellow Delayed but management involvement not necessary\n",
      "printing output text\n",
      "status is Yellow Delayed\n",
      "printing final text\n",
      "status is Yellow Delayed\n",
      "printing output text\n",
      "Delayed\n",
      "printing final text\n",
      "Delayed\n",
      "printing output text\n",
      "Project status is Yellow Delayed but management involvement not necessary.\n",
      "printing final text\n",
      "Project status is Yellow Delayed but management involvement not necessary.\n",
      "printing output text\n",
      "management involvement not necessary\n",
      "printing final text\n",
      "management involvement not necessary\n",
      "printing output text\n",
      "Yellow Delayed but management involvement not necessary.\n",
      "printing final text\n",
      "Yellow Delayed but management involvement not necessary.\n",
      "printing output text\n",
      "status is Yellow Delayed but management involvement not necessary\n",
      "printing final text\n",
      "status is Yellow Delayed but management involvement not necessary\n",
      "printing output text\n",
      "Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Delayed but management involvement not necessary\n",
      "printing output text\n",
      "management involvement not necessary.\n",
      "printing final text\n",
      "management involvement not necessary.\n",
      "printing output text\n",
      "status is Yellow Delayed but management involvement not necessary.\n",
      "printing final text\n",
      "status is Yellow Delayed but management involvement not necessary.\n",
      "printing output text\n",
      "Delayed but management involvement not necessary.\n",
      "printing final text\n",
      "Delayed but management involvement not necessary.\n",
      "printing output text\n",
      "not necessary\n",
      "printing final text\n",
      "not necessary\n",
      "printing output text\n",
      "is Yellow Delayed\n",
      "printing final text\n",
      "is Yellow Delayed\n",
      "printing output text\n",
      "slippage of 2 weeks. Project status is Yellow Delayed\n",
      "printing final text\n",
      "slippage of 2 weeks. Project status is Yellow Delayed\n",
      "printing output text\n",
      "involvement not necessary\n",
      "printing final text\n",
      "involvement not necessary\n",
      "printing output text\n",
      "not necessary.\n",
      "printing final text\n",
      "not necessary.\n",
      "printing output text\n",
      "there is slippage of 2 weeks. Project status is Yellow Delayed\n",
      "printing final text\n",
      "there is slippage of 2 weeks. Project status is Yellow Delayed\n",
      "printing output text\n",
      "6 Features were delievred. Customer has tested\n",
      "printing final text\n",
      "6 Features were delievred. Customer has tested\n",
      "printing output text\n",
      "Customer has tested\n",
      "printing final text\n",
      "Customer has tested\n",
      "printing output text\n",
      "6 Features were delievred\n",
      "printing final text\n",
      "6 Features were delievred\n",
      "printing output text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer. Also due to wrong merging\n",
      "printing final text\n",
      "Customer has tested. There is one change request from product management. 1 Defect reported from customer. Also due to wrong merging\n",
      "printing output text\n",
      "6 Features were delievred.\n",
      "printing final text\n",
      "6 Features were delievred.\n",
      "printing output text\n",
      "wrong merging\n",
      "printing final text\n",
      "wrong merging\n",
      "printing output text\n",
      "wrong merging, last sprint's features were affected\n",
      "printing final text\n",
      "wrong merging, last sprint's features were affected\n",
      "printing output text\n",
      "6 Features were delievred. Customer has tested.\n",
      "printing final text\n",
      "6 Features were delievred. Customer has tested.\n",
      "printing output text\n",
      "6\n",
      "printing final text\n",
      "6\n",
      "printing output text\n",
      "last sprint's features were affected\n",
      "printing final text\n",
      "last sprint's features were affected\n",
      "printing output text\n",
      "Features were delievred. Customer has tested\n",
      "printing final text\n",
      "Features were delievred. Customer has tested\n",
      "printing output text\n",
      "Customer has tested.\n",
      "printing final text\n",
      "Customer has tested.\n",
      "printing output text\n",
      "6 Features\n",
      "printing final text\n",
      "6 Features\n",
      "printing output text\n",
      "tested\n",
      "printing final text\n",
      "tested\n",
      "printing output text\n",
      "wrong merging, last sprint's features were affected. These defects and cleanups will require this sprint\n",
      "printing final text\n",
      "wrong merging, last sprint's features were affected. These defects and cleanups will require this sprint\n",
      "printing output text\n",
      "last sprint's features were affected. These defects and cleanups will require this sprint. Feature change request will be taken up in next sprint\n",
      "printing final text\n",
      "last sprint's features were affected. These defects and cleanups will require this sprint. Feature change request will be taken up in next sprint\n",
      "printing output text\n",
      "Feature change request will be taken up in next sprint. Overall there is slippage of 2 weeks\n",
      "printing final text\n",
      "Feature change request will be taken up in next sprint. Overall there is slippage of 2 weeks\n",
      "printing output text\n",
      "delievred. Customer has tested\n",
      "printing final text\n",
      "delievred. Customer has tested\n",
      "printing output text\n",
      "Features were delievred\n",
      "printing final text\n",
      "Features were delievred\n",
      "printing output text\n",
      "Feature change request will be taken up in next sprint\n",
      "printing final text\n",
      "Feature change request will be taken up in next sprint\n",
      "printing output text\n",
      "management involvement not necessary\n",
      "printing final text\n",
      "management involvement not necessary\n",
      "printing output text\n",
      "not necessary\n",
      "printing final text\n",
      "not necessary\n",
      "printing output text\n",
      "management involvement not necessary.\n",
      "printing final text\n",
      "management involvement not necessary.\n",
      "printing output text\n",
      "management involvement\n",
      "printing final text\n",
      "management involvement\n",
      "printing output text\n",
      "not necessary.\n",
      "printing final text\n",
      "not necessary.\n",
      "printing output text\n",
      "but management involvement not necessary\n",
      "printing final text\n",
      "but management involvement not necessary\n",
      "printing output text\n",
      "management involvement not\n",
      "printing final text\n",
      "management involvement not\n",
      "printing output text\n",
      "Project status is Yellow Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Project status is Yellow Delayed but management involvement not necessary\n",
      "printing output text\n",
      "not\n",
      "printing final text\n",
      "not\n",
      "printing output text\n",
      "involvement not necessary\n",
      "printing final text\n",
      "involvement not necessary\n",
      "printing output text\n",
      "but management involvement not necessary.\n",
      "printing final text\n",
      "but management involvement not necessary.\n",
      "printing output text\n",
      "but management involvement\n",
      "printing final text\n",
      "but management involvement\n",
      "printing output text\n",
      "Project status is Yellow Delayed but management involvement not necessary.\n",
      "printing final text\n",
      "Project status is Yellow Delayed but management involvement not necessary.\n",
      "printing output text\n",
      "Project status is Yellow Delayed but management involvement\n",
      "printing final text\n",
      "Project status is Yellow Delayed but management involvement\n",
      "printing output text\n",
      "necessary\n",
      "printing final text\n",
      "necessary\n",
      "printing output text\n",
      "involvement not necessary.\n",
      "printing final text\n",
      "involvement not necessary.\n",
      "printing output text\n",
      "involvement\n",
      "printing final text\n",
      "involvement\n",
      "printing output text\n",
      "Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Delayed but management involvement not necessary\n",
      "printing output text\n",
      "but management involvement not\n",
      "printing final text\n",
      "but management involvement not\n",
      "printing output text\n",
      "Yellow Delayed but management involvement not necessary\n",
      "printing final text\n",
      "Yellow Delayed but management involvement not necessary\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sun Mar  3 20:56:31 2019\n",
    "\n",
    "@author: Bharat\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "import argparse\n",
    "import collections\n",
    "import json\n",
    "import logging\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "import sys\n",
    "from io import open\n",
    "import easydict\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\n",
    "                              TensorDataset)\n",
    "from torch.utils.data.distributed import DistributedSampler\n",
    "from tqdm import tqdm, trange\n",
    "\n",
    "from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE\n",
    "from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig, WEIGHTS_NAME, CONFIG_NAME\n",
    "from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear\n",
    "from pytorch_pretrained_bert.tokenization import (BasicTokenizer,\n",
    "                                                  BertTokenizer,\n",
    "                                                  whitespace_tokenize)\n",
    "\n",
    "if sys.version_info[0] == 2:\n",
    "    import cPickle as pickle\n",
    "else:\n",
    "    import pickle\n",
    "\n",
    "logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',\n",
    "                    datefmt = '%m/%d/%Y %H:%M:%S',\n",
    "                    level = logging.INFO)\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "MODEL_SAVED_LOAD=True\n",
    "\n",
    "class SquadExample(object):\n",
    "    \"\"\"\n",
    "    A single training/test example for the Squad dataset.\n",
    "    For examples without an answer, the start and end position are -1.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 qas_id,\n",
    "                 question_text,\n",
    "                 doc_tokens,\n",
    "                 orig_answer_text=None,\n",
    "                 start_position=None,\n",
    "                 end_position=None,\n",
    "                 is_impossible=None):\n",
    "        self.qas_id = qas_id\n",
    "        self.question_text = question_text\n",
    "        self.doc_tokens = doc_tokens\n",
    "        self.orig_answer_text = orig_answer_text\n",
    "        self.start_position = start_position\n",
    "        self.end_position = end_position\n",
    "        self.is_impossible = is_impossible\n",
    "\n",
    "    def __str__(self):\n",
    "        return self.__repr__()\n",
    "\n",
    "    def __repr__(self):\n",
    "        s = \"\"\n",
    "        s += \"qas_id: %s\" % (self.qas_id)\n",
    "        s += \", question_text: %s\" % (\n",
    "            self.question_text)\n",
    "        s += \", doc_tokens: [%s]\" % (\" \".join(self.doc_tokens))\n",
    "        if self.start_position:\n",
    "            s += \", start_position: %d\" % (self.start_position)\n",
    "        if self.start_position:\n",
    "            s += \", end_position: %d\" % (self.end_position)\n",
    "        if self.start_position:\n",
    "            s += \", is_impossible: %r\" % (self.is_impossible)\n",
    "        return s\n",
    "\n",
    "\n",
    "class InputFeatures(object):\n",
    "    \"\"\"A single set of features of data.\"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 unique_id,\n",
    "                 example_index,\n",
    "                 doc_span_index,\n",
    "                 tokens,\n",
    "                 token_to_orig_map,\n",
    "                 token_is_max_context,\n",
    "                 input_ids,\n",
    "                 input_mask,\n",
    "                 segment_ids,\n",
    "                 start_position=None,\n",
    "                 end_position=None,\n",
    "                 is_impossible=None):\n",
    "        self.unique_id = unique_id\n",
    "        self.example_index = example_index\n",
    "        self.doc_span_index = doc_span_index\n",
    "        self.tokens = tokens\n",
    "        self.token_to_orig_map = token_to_orig_map\n",
    "        self.token_is_max_context = token_is_max_context\n",
    "        self.input_ids = input_ids\n",
    "        self.input_mask = input_mask\n",
    "        self.segment_ids = segment_ids\n",
    "        self.start_position = start_position\n",
    "        self.end_position = end_position\n",
    "        self.is_impossible = is_impossible\n",
    "\n",
    "\n",
    "def read_squad_examples(input_file, is_training, version_2_with_negative):\n",
    "    \"\"\"Read a SQuAD json file into a list of SquadExample.\"\"\"\n",
    "    with open(input_file, \"r\", encoding='utf-8') as reader:\n",
    "        input_data = json.load(reader)[\"data\"]\n",
    "\n",
    "    def is_whitespace(c):\n",
    "        if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n",
    "            return True\n",
    "        return False\n",
    "\n",
    "    examples = []\n",
    "    for entry in input_data:\n",
    "        for paragraph in entry[\"paragraphs\"]:\n",
    "            paragraph_text = paragraph[\"context\"]\n",
    "            doc_tokens = []\n",
    "            char_to_word_offset = []\n",
    "            prev_is_whitespace = True\n",
    "            for c in paragraph_text:\n",
    "                if is_whitespace(c):\n",
    "                    prev_is_whitespace = True\n",
    "                else:\n",
    "                    if prev_is_whitespace:\n",
    "                        doc_tokens.append(c)\n",
    "                    else:\n",
    "                        doc_tokens[-1] += c\n",
    "                    prev_is_whitespace = False\n",
    "                char_to_word_offset.append(len(doc_tokens) - 1)\n",
    "\n",
    "            for qa in paragraph[\"qas\"]:\n",
    "                qas_id = qa[\"id\"]\n",
    "                question_text = qa[\"question\"]\n",
    "                start_position = None\n",
    "                end_position = None\n",
    "                orig_answer_text = None\n",
    "                is_impossible = False\n",
    "                if is_training:\n",
    "                    if version_2_with_negative:\n",
    "                        is_impossible = qa[\"is_impossible\"]\n",
    "                    if (len(qa[\"answers\"]) != 1) and (not is_impossible):\n",
    "                        raise ValueError(   \"For training, each question should have exactly 1 answer.\")\n",
    "                    if not is_impossible:\n",
    "                        answer = qa[\"answers\"][0]\n",
    "                        orig_answer_text = answer[\"text\"]\n",
    "                        answer_offset = answer[\"answer_start\"]\n",
    "                        answer_length = len(orig_answer_text)\n",
    "                        start_position = char_to_word_offset[answer_offset]\n",
    "                        end_position = char_to_word_offset[answer_offset + answer_length - 1]\n",
    "                        # Only add answers where the text can be exactly recovered from the\n",
    "                        # document. If this CAN'T happen it's likely due to weird Unicode\n",
    "                        # stuff so we will just skip the example.\n",
    "                        #\n",
    "                        # Note that this means for training mode, every example is NOT\n",
    "                        # guaranteed to be preserved.\n",
    "                        actual_text = \" \".join(doc_tokens[start_position:(end_position + 1)])\n",
    "                        cleaned_answer_text = \" \".join(\n",
    "                            whitespace_tokenize(orig_answer_text))\n",
    "                        if actual_text.find(cleaned_answer_text) == -1:\n",
    "                            logger.warning(\"Could not find answer: '%s' vs. '%s'\",\n",
    "                                           actual_text, cleaned_answer_text)\n",
    "                            continue\n",
    "                    else:\n",
    "                        start_position = -1\n",
    "                        end_position = -1\n",
    "                        orig_answer_text = \"\"\n",
    "\n",
    "                example = SquadExample(\n",
    "                    qas_id=qas_id,\n",
    "                    question_text=question_text,\n",
    "                    doc_tokens=doc_tokens,\n",
    "                    orig_answer_text=orig_answer_text,\n",
    "                    start_position=start_position,\n",
    "                    end_position=end_position,\n",
    "                    is_impossible=is_impossible)\n",
    "                examples.append(example)\n",
    "    return examples\n",
    "\n",
    "def convert_examples_to_features(examples, tokenizer, max_seq_length,\n",
    "                                 doc_stride, max_query_length, is_training):\n",
    "    \"\"\"Loads a data file into a list of `InputBatch`s.\"\"\"\n",
    "\n",
    "    unique_id = 1000000000\n",
    "\n",
    "    features = []\n",
    "    for (example_index, example) in enumerate(examples):\n",
    "        query_tokens = tokenizer.tokenize(example.question_text)\n",
    "\n",
    "        if len(query_tokens) > max_query_length:\n",
    "            query_tokens = query_tokens[0:max_query_length]\n",
    "\n",
    "        tok_to_orig_index = []\n",
    "        orig_to_tok_index = []\n",
    "        all_doc_tokens = []\n",
    "        for (i, token) in enumerate(example.doc_tokens):\n",
    "            orig_to_tok_index.append(len(all_doc_tokens))\n",
    "            sub_tokens = tokenizer.tokenize(token)\n",
    "            for sub_token in sub_tokens:\n",
    "                tok_to_orig_index.append(i)\n",
    "                all_doc_tokens.append(sub_token)\n",
    "\n",
    "        tok_start_position = None\n",
    "        tok_end_position = None\n",
    "        if is_training and example.is_impossible:\n",
    "            tok_start_position = -1\n",
    "            tok_end_position = -1\n",
    "        if is_training and not example.is_impossible:\n",
    "            tok_start_position = orig_to_tok_index[example.start_position]\n",
    "            if example.end_position < len(example.doc_tokens) - 1:\n",
    "                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\n",
    "            else:\n",
    "                tok_end_position = len(all_doc_tokens) - 1\n",
    "            (tok_start_position, tok_end_position) = _improve_answer_span(\n",
    "                all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\n",
    "                example.orig_answer_text)\n",
    "\n",
    "        # The -3 accounts for [CLS], [SEP] and [SEP]\n",
    "        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\n",
    "\n",
    "        # We can have documents that are longer than the maximum sequence length.\n",
    "        # To deal with this we do a sliding window approach, where we take chunks\n",
    "        # of the up to our max length with a stride of `doc_stride`.\n",
    "        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name\n",
    "            \"DocSpan\", [\"start\", \"length\"])\n",
    "        doc_spans = []\n",
    "        start_offset = 0\n",
    "        while start_offset < len(all_doc_tokens):\n",
    "            length = len(all_doc_tokens) - start_offset\n",
    "            if length > max_tokens_for_doc:\n",
    "                length = max_tokens_for_doc\n",
    "            doc_spans.append(_DocSpan(start=start_offset, length=length))\n",
    "            if start_offset + length == len(all_doc_tokens):\n",
    "                break\n",
    "            start_offset += min(length, doc_stride)\n",
    "\n",
    "        for (doc_span_index, doc_span) in enumerate(doc_spans):\n",
    "            tokens = []\n",
    "            token_to_orig_map = {}\n",
    "            token_is_max_context = {}\n",
    "            segment_ids = []\n",
    "            tokens.append(\"[CLS]\")\n",
    "            segment_ids.append(0)\n",
    "            for token in query_tokens:\n",
    "                tokens.append(token)\n",
    "                segment_ids.append(0)\n",
    "            tokens.append(\"[SEP]\")\n",
    "            segment_ids.append(0)\n",
    "\n",
    "            for i in range(doc_span.length):\n",
    "                split_token_index = doc_span.start + i\n",
    "                token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n",
    "\n",
    "                is_max_context = _check_is_max_context(doc_spans, doc_span_index,\n",
    "                                                       split_token_index)\n",
    "                token_is_max_context[len(tokens)] = is_max_context\n",
    "                tokens.append(all_doc_tokens[split_token_index])\n",
    "                segment_ids.append(1)\n",
    "            tokens.append(\"[SEP]\")\n",
    "            segment_ids.append(1)\n",
    "\n",
    "            input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "\n",
    "            # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
    "            # tokens are attended to.\n",
    "            input_mask = [1] * len(input_ids)\n",
    "\n",
    "            # Zero-pad up to the sequence length.\n",
    "            while len(input_ids) < max_seq_length:\n",
    "                input_ids.append(0)\n",
    "                input_mask.append(0)\n",
    "                segment_ids.append(0)\n",
    "\n",
    "            assert len(input_ids) == max_seq_length\n",
    "            assert len(input_mask) == max_seq_length\n",
    "            assert len(segment_ids) == max_seq_length\n",
    "\n",
    "            start_position = None\n",
    "            end_position = None\n",
    "            if is_training and not example.is_impossible:\n",
    "                # For training, if our document chunk does not contain an annotation\n",
    "                # we throw it out, since there is nothing to predict.\n",
    "                doc_start = doc_span.start\n",
    "                doc_end = doc_span.start + doc_span.length - 1\n",
    "                out_of_span = False\n",
    "                if not (tok_start_position >= doc_start and\n",
    "                        tok_end_position <= doc_end):\n",
    "                    out_of_span = True\n",
    "                if out_of_span:\n",
    "                    start_position = 0\n",
    "                    end_position = 0\n",
    "                else:\n",
    "                    doc_offset = len(query_tokens) + 2\n",
    "                    start_position = tok_start_position - doc_start + doc_offset\n",
    "                    end_position = tok_end_position - doc_start + doc_offset\n",
    "            if is_training and example.is_impossible:\n",
    "                start_position = 0\n",
    "                end_position = 0\n",
    "            if example_index < 20:\n",
    "                logger.info(\"*** Example ***\")\n",
    "                logger.info(\"unique_id: %s\" % (unique_id))\n",
    "                logger.info(\"example_index: %s\" % (example_index))\n",
    "                logger.info(\"doc_span_index: %s\" % (doc_span_index))\n",
    "                logger.info(\"tokens: %s\" % \" \".join(tokens))\n",
    "                logger.info(\"token_to_orig_map: %s\" % \" \".join([\n",
    "                    \"%d:%d\" % (x, y) for (x, y) in token_to_orig_map.items()]))\n",
    "                logger.info(\"token_is_max_context: %s\" % \" \".join([\n",
    "                    \"%d:%s\" % (x, y) for (x, y) in token_is_max_context.items()\n",
    "                ]))\n",
    "                logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n",
    "                logger.info(\n",
    "                    \"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n",
    "                logger.info(\n",
    "                    \"segment_ids: %s\" % \" \".join([str(x) for x in segment_ids]))\n",
    "                if is_training and example.is_impossible:\n",
    "                    logger.info(\"impossible example\")\n",
    "                if is_training and not example.is_impossible:\n",
    "                    answer_text = \" \".join(tokens[start_position:(end_position + 1)])\n",
    "                    logger.info(\"start_position: %d\" % (start_position))\n",
    "                    logger.info(\"end_position: %d\" % (end_position))\n",
    "                    logger.info(\n",
    "                        \"answer: %s\" % (answer_text))\n",
    "\n",
    "            features.append(\n",
    "                InputFeatures(\n",
    "                    unique_id=unique_id,\n",
    "                    example_index=example_index,\n",
    "                    doc_span_index=doc_span_index,\n",
    "                    tokens=tokens,\n",
    "                    token_to_orig_map=token_to_orig_map,\n",
    "                    token_is_max_context=token_is_max_context,\n",
    "                    input_ids=input_ids,\n",
    "                    input_mask=input_mask,\n",
    "                    segment_ids=segment_ids,\n",
    "                    start_position=start_position,\n",
    "                    end_position=end_position,\n",
    "                    is_impossible=example.is_impossible))\n",
    "            unique_id += 1\n",
    "\n",
    "    return features\n",
    "\n",
    "def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,\n",
    "                         orig_answer_text):\n",
    "    \"\"\"Returns tokenized answer spans that better match the annotated answer.\"\"\"\n",
    "\n",
    "    # The SQuAD annotations are character based. We first project them to\n",
    "    # whitespace-tokenized words. But then after WordPiece tokenization, we can\n",
    "    # often find a \"better match\". For example:\n",
    "    #\n",
    "    #   Question: What year was John Smith born?\n",
    "    #   Context: The leader was John Smith (1895-1943).\n",
    "    #   Answer: 1895\n",
    "    #\n",
    "    # The original whitespace-tokenized answer will be \"(1895-1943).\". However\n",
    "    # after tokenization, our tokens will be \"( 1895 - 1943 ) .\". So we can match\n",
    "    # the exact answer, 1895.\n",
    "    #\n",
    "    # However, this is not always possible. Consider the following:\n",
    "    #\n",
    "    #   Question: What country is the top exporter of electornics?\n",
    "    #   Context: The Japanese electronics industry is the lagest in the world.\n",
    "    #   Answer: Japan\n",
    "    #\n",
    "    # In this case, the annotator chose \"Japan\" as a character sub-span of\n",
    "    # the word \"Japanese\". Since our WordPiece tokenizer does not split\n",
    "    # \"Japanese\", we just use \"Japanese\" as the annotation. This is fairly rare\n",
    "    # in SQuAD, but does happen.\n",
    "    tok_answer_text = \" \".join(tokenizer.tokenize(orig_answer_text))\n",
    "\n",
    "    for new_start in range(input_start, input_end + 1):\n",
    "        for new_end in range(input_end, new_start - 1, -1):\n",
    "            text_span = \" \".join(doc_tokens[new_start:(new_end + 1)])\n",
    "            if text_span == tok_answer_text:\n",
    "                return (new_start, new_end)\n",
    "\n",
    "    return (input_start, input_end)\n",
    "\n",
    "\n",
    "def _check_is_max_context(doc_spans, cur_span_index, position):\n",
    "    \"\"\"Check if this is the 'max context' doc span for the token.\"\"\"\n",
    "\n",
    "    # Because of the sliding window approach taken to scoring documents, a single\n",
    "    # token can appear in multiple documents. E.g.\n",
    "    #  Doc: the man went to the store and bought a gallon of milk\n",
    "    #  Span A: the man went to the\n",
    "    #  Span B: to the store and bought\n",
    "    #  Span C: and bought a gallon of\n",
    "    #  ...\n",
    "    #\n",
    "    # Now the word 'bought' will have two scores from spans B and C. We only\n",
    "    # want to consider the score with \"maximum context\", which we define as\n",
    "    # the *minimum* of its left and right context (the *sum* of left and\n",
    "    # right context will always be the same, of course).\n",
    "    #\n",
    "    # In the example the maximum context for 'bought' would be span C since\n",
    "    # it has 1 left context and 3 right context, while span B has 4 left context\n",
    "    # and 0 right context.\n",
    "    best_score = None\n",
    "    best_span_index = None\n",
    "    for (span_index, doc_span) in enumerate(doc_spans):\n",
    "        end = doc_span.start + doc_span.length - 1\n",
    "        if position < doc_span.start:\n",
    "            continue\n",
    "        if position > end:\n",
    "            continue\n",
    "        num_left_context = position - doc_span.start\n",
    "        num_right_context = end - position\n",
    "        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length\n",
    "        if best_score is None or score > best_score:\n",
    "            best_score = score\n",
    "            best_span_index = span_index\n",
    "\n",
    "    return cur_span_index == best_span_index\n",
    "\n",
    "\n",
    "RawResult = collections.namedtuple(\"RawResult\",\n",
    "                                   [\"unique_id\", \"start_logits\", \"end_logits\"])\n",
    "\n",
    "\n",
    "def write_predictions(all_examples, all_features, all_results, n_best_size,\n",
    "                      max_answer_length, do_lower_case, output_prediction_file,\n",
    "                      output_nbest_file, output_null_log_odds_file, verbose_logging,\n",
    "                      version_2_with_negative, null_score_diff_threshold):\n",
    "    \"\"\"Write final predictions to the json file and log-odds of null if needed.\"\"\"\n",
    "    logger.info(\"Writing predictions to: %s\" % (output_prediction_file))\n",
    "    logger.info(\"Writing nbest to: %s\" % (output_nbest_file))\n",
    "\n",
    "    example_index_to_features = collections.defaultdict(list)\n",
    "    for feature in all_features:\n",
    "        example_index_to_features[feature.example_index].append(feature)\n",
    "\n",
    "    unique_id_to_result = {}\n",
    "    for result in all_results:\n",
    "        unique_id_to_result[result.unique_id] = result\n",
    "\n",
    "    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name\n",
    "        \"PrelimPrediction\",\n",
    "        [\"feature_index\", \"start_index\", \"end_index\", \"start_logit\", \"end_logit\"])\n",
    "\n",
    "    all_predictions = collections.OrderedDict()\n",
    "    all_nbest_json = collections.OrderedDict()\n",
    "    scores_diff_json = collections.OrderedDict()\n",
    "\n",
    "    for (example_index, example) in enumerate(all_examples):\n",
    "        features = example_index_to_features[example_index]\n",
    "\n",
    "        prelim_predictions = []\n",
    "        # keep track of the minimum score of null start+end of position 0\n",
    "        score_null = 1000000  # large and positive\n",
    "        min_null_feature_index = 0  # the paragraph slice with min mull score\n",
    "        null_start_logit = 0  # the start logit at the slice with min null score\n",
    "        null_end_logit = 0  # the end logit at the slice with min null score\n",
    "        for (feature_index, feature) in enumerate(features):\n",
    "            result = unique_id_to_result[feature.unique_id]\n",
    "            start_indexes = _get_best_indexes(result.start_logits, n_best_size)\n",
    "            end_indexes = _get_best_indexes(result.end_logits, n_best_size)\n",
    "            # if we could have irrelevant answers, get the min score of irrelevant\n",
    "            if version_2_with_negative:\n",
    "                feature_null_score = result.start_logits[0] + result.end_logits[0]\n",
    "                if feature_null_score < score_null:\n",
    "                    score_null = feature_null_score\n",
    "                    min_null_feature_index = feature_index\n",
    "                    null_start_logit = result.start_logits[0]\n",
    "                    null_end_logit = result.end_logits[0]\n",
    "            for start_index in start_indexes:\n",
    "                for end_index in end_indexes:\n",
    "                    # We could hypothetically create invalid predictions, e.g., predict\n",
    "                    # that the start of the span is in the question. We throw out all\n",
    "                    # invalid predictions.\n",
    "                    if start_index >= len(feature.tokens):\n",
    "                        continue\n",
    "                    if end_index >= len(feature.tokens):\n",
    "                        continue\n",
    "                    if start_index not in feature.token_to_orig_map:\n",
    "                        continue\n",
    "                    if end_index not in feature.token_to_orig_map:\n",
    "                        continue\n",
    "                    if not feature.token_is_max_context.get(start_index, False):\n",
    "                        continue\n",
    "                    if end_index < start_index:\n",
    "                        continue\n",
    "                    length = end_index - start_index + 1\n",
    "                    if length > max_answer_length:\n",
    "                        continue\n",
    "                    prelim_predictions.append(\n",
    "                        _PrelimPrediction(\n",
    "                            feature_index=feature_index,\n",
    "                            start_index=start_index,\n",
    "                            end_index=end_index,\n",
    "                            start_logit=result.start_logits[start_index],\n",
    "                            end_logit=result.end_logits[end_index]))\n",
    "        if version_2_with_negative:\n",
    "            prelim_predictions.append(\n",
    "                _PrelimPrediction(\n",
    "                    feature_index=min_null_feature_index,\n",
    "                    start_index=0,\n",
    "                    end_index=0,\n",
    "                    start_logit=null_start_logit,\n",
    "                    end_logit=null_end_logit))\n",
    "        prelim_predictions = sorted(\n",
    "            prelim_predictions,\n",
    "            key=lambda x: (x.start_logit + x.end_logit),\n",
    "            reverse=True)\n",
    "\n",
    "        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name\n",
    "            \"NbestPrediction\", [\"text\", \"start_logit\", \"end_logit\"])\n",
    "\n",
    "        seen_predictions = {}\n",
    "        nbest = []\n",
    "        for pred in prelim_predictions:\n",
    "            if len(nbest) >= n_best_size:\n",
    "                break\n",
    "            feature = features[pred.feature_index]\n",
    "            if pred.start_index > 0:  # this is a non-null prediction\n",
    "                tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]\n",
    "                orig_doc_start = feature.token_to_orig_map[pred.start_index]\n",
    "                orig_doc_end = feature.token_to_orig_map[pred.end_index]\n",
    "                orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]\n",
    "                tok_text = \" \".join(tok_tokens)\n",
    "\n",
    "                # De-tokenize WordPieces that have been split off.\n",
    "                tok_text = tok_text.replace(\" ##\", \"\")\n",
    "                tok_text = tok_text.replace(\"##\", \"\")\n",
    "\n",
    "                # Clean whitespace\n",
    "                tok_text = tok_text.strip()\n",
    "                tok_text = \" \".join(tok_text.split())\n",
    "                orig_text = \" \".join(orig_tokens)\n",
    "\n",
    "                final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)\n",
    "                print(\"printing final text\")\n",
    "                print(final_text)\n",
    "                if final_text in seen_predictions:\n",
    "                    continue\n",
    "\n",
    "                seen_predictions[final_text] = True\n",
    "            else:\n",
    "                final_text = \"\"\n",
    "                seen_predictions[final_text] = True\n",
    "\n",
    "            nbest.append(\n",
    "                _NbestPrediction(\n",
    "                    text=final_text,\n",
    "                    start_logit=pred.start_logit,\n",
    "                    end_logit=pred.end_logit))\n",
    "        # if we didn't include the empty option in the n-best, include it\n",
    "        if version_2_with_negative:\n",
    "            if \"\" not in seen_predictions:\n",
    "                nbest.append(\n",
    "                    _NbestPrediction(\n",
    "                        text=\"\",\n",
    "                        start_logit=null_start_logit,\n",
    "                        end_logit=null_end_logit))\n",
    "                \n",
    "            # In very rare edge cases we could only have single null prediction.\n",
    "            # So we just create a nonce prediction in this case to avoid failure.\n",
    "            if len(nbest)==1:\n",
    "                nbest.insert(0,\n",
    "                    _NbestPrediction(text=\"empty\", start_logit=0.0, end_logit=0.0))\n",
    "                \n",
    "        # In very rare edge cases we could have no valid predictions. So we\n",
    "        # just create a nonce prediction in this case to avoid failure.\n",
    "        if not nbest:\n",
    "            nbest.append(\n",
    "                _NbestPrediction(text=\"empty\", start_logit=0.0, end_logit=0.0))\n",
    "\n",
    "        assert len(nbest) >= 1\n",
    "\n",
    "        total_scores = []\n",
    "        best_non_null_entry = None\n",
    "        for entry in nbest:\n",
    "            total_scores.append(entry.start_logit + entry.end_logit)\n",
    "            if not best_non_null_entry:\n",
    "                if entry.text:\n",
    "                    best_non_null_entry = entry\n",
    "\n",
    "        probs = _compute_softmax(total_scores)\n",
    "\n",
    "        nbest_json = []\n",
    "        for (i, entry) in enumerate(nbest):\n",
    "            output = collections.OrderedDict()\n",
    "            output[\"text\"] = entry.text\n",
    "            output[\"probability\"] = probs[i]\n",
    "            output[\"start_logit\"] = entry.start_logit\n",
    "            output[\"end_logit\"] = entry.end_logit\n",
    "            nbest_json.append(output)\n",
    "\n",
    "        assert len(nbest_json) >= 1\n",
    "\n",
    "        if not version_2_with_negative:\n",
    "            all_predictions[example.qas_id] = nbest_json[0][\"text\"]\n",
    "        else:\n",
    "            # predict \"\" iff the null score - the score of best non-null > threshold\n",
    "            score_diff = score_null - best_non_null_entry.start_logit - (\n",
    "                best_non_null_entry.end_logit)\n",
    "            scores_diff_json[example.qas_id] = score_diff\n",
    "            if score_diff > null_score_diff_threshold:\n",
    "                all_predictions[example.qas_id] = \"\"\n",
    "            else:\n",
    "                all_predictions[example.qas_id] = best_non_null_entry.text\n",
    "                all_nbest_json[example.qas_id] = nbest_json\n",
    "\n",
    "    with open(output_prediction_file, \"w\") as writer:\n",
    "        writer.write(json.dumps(all_predictions, indent=4) + \"\\n\")\n",
    "\n",
    "    with open(output_nbest_file, \"w\") as writer:\n",
    "        writer.write(json.dumps(all_nbest_json, indent=4) + \"\\n\")\n",
    "\n",
    "    if version_2_with_negative:\n",
    "        with open(output_null_log_odds_file, \"w\") as writer:\n",
    "            writer.write(json.dumps(scores_diff_json, indent=4) + \"\\n\")\n",
    "\n",
    "\n",
    "def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):\n",
    "    \"\"\"Project the tokenized prediction back to the original text.\"\"\"\n",
    "\n",
    "    # When we created the data, we kept track of the alignment between original\n",
    "    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So\n",
    "    # now `orig_text` contains the span of our original text corresponding to the\n",
    "    # span that we predicted.\n",
    "    #\n",
    "    # However, `orig_text` may contain extra characters that we don't want in\n",
    "    # our prediction.\n",
    "    #\n",
    "    # For example, let's say:\n",
    "    #   pred_text = steve smith\n",
    "    #   orig_text = Steve Smith's\n",
    "    #\n",
    "    # We don't want to return `orig_text` because it contains the extra \"'s\".\n",
    "    #\n",
    "    # We don't want to return `pred_text` because it's already been normalized\n",
    "    # (the SQuAD eval script also does punctuation stripping/lower casing but\n",
    "    # our tokenizer does additional normalization like stripping accent\n",
    "    # characters).\n",
    "    #\n",
    "    # What we really want to return is \"Steve Smith\".\n",
    "    #\n",
    "    # Therefore, we have to apply a semi-complicated alignment heruistic between\n",
    "    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This\n",
    "    # can fail in certain cases in which case we just return `orig_text`.\n",
    "\n",
    "    def _strip_spaces(text):\n",
    "        ns_chars = []\n",
    "        ns_to_s_map = collections.OrderedDict()\n",
    "        for (i, c) in enumerate(text):\n",
    "            if c == \" \":\n",
    "                continue\n",
    "            ns_to_s_map[len(ns_chars)] = i\n",
    "            ns_chars.append(c)\n",
    "        ns_text = \"\".join(ns_chars)\n",
    "        return (ns_text, ns_to_s_map)\n",
    "\n",
    "    # We first tokenize `orig_text`, strip whitespace from the result\n",
    "    # and `pred_text`, and check if they are the same length. If they are\n",
    "    # NOT the same length, the heuristic has failed. If they are the same\n",
    "    # length, we assume the characters are one-to-one aligned.\n",
    "    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)\n",
    "\n",
    "    tok_text = \" \".join(tokenizer.tokenize(orig_text))\n",
    "\n",
    "    start_position = tok_text.find(pred_text)\n",
    "    if start_position == -1:\n",
    "        if verbose_logging:\n",
    "            logger.info(\n",
    "                \"Unable to find text: '%s' in '%s'\" % (pred_text, orig_text))\n",
    "        return orig_text\n",
    "    end_position = start_position + len(pred_text) - 1\n",
    "\n",
    "    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)\n",
    "    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)\n",
    "\n",
    "    if len(orig_ns_text) != len(tok_ns_text):\n",
    "        if verbose_logging:\n",
    "            logger.info(\"Length not equal after stripping spaces: '%s' vs '%s'\",\n",
    "                        orig_ns_text, tok_ns_text)\n",
    "        return orig_text\n",
    "\n",
    "    # We then project the characters in `pred_text` back to `orig_text` using\n",
    "    # the character-to-character alignment.\n",
    "    tok_s_to_ns_map = {}\n",
    "    for (i, tok_index) in tok_ns_to_s_map.items():\n",
    "        tok_s_to_ns_map[tok_index] = i\n",
    "\n",
    "    orig_start_position = None\n",
    "    if start_position in tok_s_to_ns_map:\n",
    "        ns_start_position = tok_s_to_ns_map[start_position]\n",
    "        if ns_start_position in orig_ns_to_s_map:\n",
    "            orig_start_position = orig_ns_to_s_map[ns_start_position]\n",
    "\n",
    "    if orig_start_position is None:\n",
    "        if verbose_logging:\n",
    "            logger.info(\"Couldn't map start position\")\n",
    "        return orig_text\n",
    "\n",
    "    orig_end_position = None\n",
    "    if end_position in tok_s_to_ns_map:\n",
    "        ns_end_position = tok_s_to_ns_map[end_position]\n",
    "        if ns_end_position in orig_ns_to_s_map:\n",
    "            orig_end_position = orig_ns_to_s_map[ns_end_position]\n",
    "\n",
    "    if orig_end_position is None:\n",
    "        if verbose_logging:\n",
    "            logger.info(\"Couldn't map end position\")\n",
    "        return orig_text\n",
    "\n",
    "    output_text = orig_text[orig_start_position:(orig_end_position + 1)]\n",
    "    print(\"printing output text\")\n",
    "    print(output_text)\n",
    "    return output_text\n",
    "\n",
    "\n",
    "def _get_best_indexes(logits, n_best_size):\n",
    "    \"\"\"Get the n-best logits from a list.\"\"\"\n",
    "    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    best_indexes = []\n",
    "    for i in range(len(index_and_score)):\n",
    "        if i >= n_best_size:\n",
    "            break\n",
    "        best_indexes.append(index_and_score[i][0])\n",
    "    return best_indexes\n",
    "\n",
    "def _compute_softmax(scores):\n",
    "    \"\"\"Compute softmax probability over raw logits.\"\"\"\n",
    "    if not scores:\n",
    "        return []\n",
    "\n",
    "    max_score = None\n",
    "    for score in scores:\n",
    "        if max_score is None or score > max_score:\n",
    "            max_score = score\n",
    "\n",
    "    exp_scores = []\n",
    "    total_sum = 0.0\n",
    "    for score in scores:\n",
    "        x = math.exp(score - max_score)\n",
    "        exp_scores.append(x)\n",
    "        total_sum += x\n",
    "\n",
    "    probs = []\n",
    "    for score in exp_scores:\n",
    "        probs.append(score / total_sum)\n",
    "    return probs\n",
    "\n",
    "def main():\n",
    "    args = easydict.EasyDict({\n",
    "    \"bert_model\": 'bert-large-uncased',\n",
    "    \"output_dir\": \"//home//belgundlibharat//Output\",\n",
    "    \"train_file\": '//home//belgundlibharat//train-v1.1.json',\n",
    "    \"predict_file\": '//home//belgundlibharat//test.json',\n",
    "\t\"max_seq_length\": 384 ,\n",
    "\t\"doc_stride\": 128,\n",
    "\t\"max_query_length\": 64,\n",
    "\t\"do_train\": False,\n",
    "\t\"do_predict\":True,\n",
    "\t\"train_batch_size\":8,\n",
    "\t\"predict_batch_size\":8,\n",
    "\t\"learning_rate\": 3e-5,\n",
    "\t\"num_train_epochs\": 2.0,\n",
    "\t\"warmup_proportion\": 0.1,\n",
    "\t\"n_best_size\": 20,\n",
    "\t\"max_answer_length\": 30,\n",
    "\t\"verbose_logging\":True,\n",
    "\t\"no_cuda\":False,\n",
    "\t\"seed\": 42,\n",
    "\t\"gradient_accumulation_steps\": 1,\n",
    "\t\"do_lower_case\":True,\n",
    "\t\"local_rank\": -1,\n",
    "\t\"fp16\":False,\n",
    "\t\"loss_scale\": 0,\n",
    "\t\"version_2_with_negative\":False,\n",
    "\t\"null_score_diff_threshold\": 0.0\n",
    "\t})\n",
    "\n",
    "    if args.local_rank == -1 or args.no_cuda:\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n",
    "        n_gpu = torch.cuda.device_count()\n",
    "    else:\n",
    "        torch.cuda.set_device(args.local_rank)\n",
    "        device = torch.device(\"cuda\", args.local_rank)\n",
    "        n_gpu = 1\n",
    "        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs\n",
    "        torch.distributed.init_process_group(backend='nccl')\n",
    "    logger.info(\"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}\".format(\n",
    "        device, n_gpu, bool(args.local_rank != -1), args.fp16))\n",
    "\n",
    "    if args.gradient_accumulation_steps < 1:\n",
    "        raise ValueError(\"Invalid gradient_accumulation_steps parameter: {}, should be >= 1\".format(\n",
    "                            args.gradient_accumulation_steps))\n",
    "\n",
    "    args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps\n",
    "\n",
    "    random.seed(args.seed)\n",
    "    np.random.seed(args.seed)\n",
    "    torch.manual_seed(args.seed)\n",
    "    if n_gpu > 0:\n",
    "        torch.cuda.manual_seed_all(args.seed)\n",
    "\n",
    "    if not args.do_train and not args.do_predict:\n",
    "        raise ValueError(\"At least one of `do_train` or `do_predict` must be True.\")\n",
    "\n",
    "    if args.do_train:\n",
    "        if not args.train_file:\n",
    "            raise ValueError(\n",
    "                \"If `do_train` is True, then `train_file` must be specified.\")\n",
    "    if args.do_predict:\n",
    "        if not args.predict_file:\n",
    "            raise ValueError(\n",
    "                \"If `do_predict` is True, then `predict_file` must be specified.\")\n",
    "\n",
    "    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:\n",
    "        raise ValueError(\"Output directory () already exists and is not empty.\")\n",
    "    if not os.path.exists(args.output_dir):\n",
    "        os.makedirs(args.output_dir)\n",
    "\n",
    "    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)\n",
    "\n",
    "    train_examples = None\n",
    "    num_train_optimization_steps = None\n",
    "    if args.do_train:\n",
    "        train_examples = read_squad_examples(\n",
    "            input_file=args.train_file, is_training=True, version_2_with_negative=args.version_2_with_negative)\n",
    "        num_train_optimization_steps = int(\n",
    "            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs\n",
    "        if args.local_rank != -1:\n",
    "            num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()\n",
    "\n",
    "    # Prepare model\n",
    "    model = BertForQuestionAnswering.from_pretrained(args.bert_model,\n",
    "                cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)))\n",
    "\n",
    "    if args.fp16:\n",
    "        model.half()\n",
    "    model.to(device)\n",
    "    if args.local_rank != -1:\n",
    "        try:\n",
    "            from apex.parallel import DistributedDataParallel as DDP\n",
    "        except ImportError:\n",
    "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.\")\n",
    "\n",
    "        model = DDP(model)\n",
    "    elif n_gpu > 1:\n",
    "        model = torch.nn.DataParallel(model)\n",
    "\n",
    "    # Prepare optimizer\n",
    "    param_optimizer = list(model.named_parameters())\n",
    "\n",
    "    # hack to remove pooler, which is not used\n",
    "    # thus it produce None grad that break apex\n",
    "    param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]\n",
    "\n",
    "    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "    optimizer_grouped_parameters = [\n",
    "        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
    "        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "        ]\n",
    "\n",
    "    if args.fp16:\n",
    "        try:\n",
    "            from apex.optimizers import FP16_Optimizer\n",
    "            from apex.optimizers import FusedAdam\n",
    "        except ImportError:\n",
    "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.\")\n",
    "\n",
    "        optimizer = FusedAdam(optimizer_grouped_parameters,\n",
    "                              lr=args.learning_rate,\n",
    "                              bias_correction=False,\n",
    "                              max_grad_norm=1.0)\n",
    "        if args.loss_scale == 0:\n",
    "            optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)\n",
    "        else:\n",
    "            optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)\n",
    "    else:\n",
    "        optimizer = BertAdam(optimizer_grouped_parameters,\n",
    "                             lr=args.learning_rate,\n",
    "                             warmup=args.warmup_proportion,\n",
    "                             t_total=num_train_optimization_steps)\n",
    "\n",
    "    global_step = 0\n",
    "    if args.do_train:\n",
    "        cached_train_features_file = args.train_file+'_{0}_{1}_{2}_{3}'.format(\n",
    "            list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length))\n",
    "        train_features = None\n",
    "        try:\n",
    "            with open(cached_train_features_file, \"rb\") as reader:\n",
    "                train_features = pickle.load(reader)\n",
    "        except:\n",
    "            train_features = convert_examples_to_features(\n",
    "                examples=train_examples,\n",
    "                tokenizer=tokenizer,\n",
    "                max_seq_length=args.max_seq_length,\n",
    "                doc_stride=args.doc_stride,\n",
    "                max_query_length=args.max_query_length,\n",
    "                is_training=True)\n",
    "            if args.local_rank == -1 or torch.distributed.get_rank() == 0:\n",
    "                logger.info(\"  Saving train features into cached file %s\", cached_train_features_file)\n",
    "                with open(cached_train_features_file, \"wb\") as writer:\n",
    "                    pickle.dump(train_features, writer)\n",
    "        logger.info(\"***** Running training *****\")\n",
    "        logger.info(\"  Num orig examples = %d\", len(train_examples))\n",
    "        logger.info(\"  Num split examples = %d\", len(train_features))\n",
    "        logger.info(\"  Batch size = %d\", args.train_batch_size)\n",
    "        logger.info(\"  Num steps = %d\", num_train_optimization_steps)\n",
    "        all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)\n",
    "        all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)\n",
    "        all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)\n",
    "        all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)\n",
    "        all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)\n",
    "        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,\n",
    "                                   all_start_positions, all_end_positions)\n",
    "        if args.local_rank == -1:\n",
    "            train_sampler = RandomSampler(train_data)\n",
    "        else:\n",
    "            train_sampler = DistributedSampler(train_data)\n",
    "        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)\n",
    "\n",
    "        model.train()\n",
    "        for _ in trange(int(args.num_train_epochs), desc=\"Epoch\"):\n",
    "            for step, batch in enumerate(tqdm(train_dataloader, desc=\"Iteration\")):\n",
    "                if n_gpu == 1:\n",
    "                    batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self\n",
    "                input_ids, input_mask, segment_ids, start_positions, end_positions = batch\n",
    "                loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)\n",
    "                if n_gpu > 1:\n",
    "                    loss = loss.mean() # mean() to average on multi-gpu.\n",
    "                if args.gradient_accumulation_steps > 1:\n",
    "                    loss = loss / args.gradient_accumulation_steps\n",
    "\n",
    "                if args.fp16:\n",
    "                    optimizer.backward(loss)\n",
    "                else:\n",
    "                    loss.backward()\n",
    "                if (step + 1) % args.gradient_accumulation_steps == 0:\n",
    "                    if args.fp16:\n",
    "                        # modify learning rate with special warm up BERT uses\n",
    "                        # if args.fp16 is False, BertAdam is used and handles this automatically\n",
    "                        lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)\n",
    "                        for param_group in optimizer.param_groups:\n",
    "                            param_group['lr'] = lr_this_step\n",
    "                    optimizer.step()\n",
    "                    optimizer.zero_grad()\n",
    "                    global_step += 1\n",
    "\n",
    "    if args.do_train:\n",
    "        # Save a trained model and the associated configuration\n",
    "        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self\n",
    "        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)\n",
    "        torch.save(model_to_save.state_dict(), output_model_file)\n",
    "        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)\n",
    "        with open(output_config_file, 'w') as f:\n",
    "            f.write(model_to_save.config.to_json_string())\n",
    "\n",
    "        # Load a trained model and config that you have fine-tuned\n",
    "        config = BertConfig(output_config_file)\n",
    "        model = BertForQuestionAnswering(config)\n",
    "        model.load_state_dict(torch.load(output_model_file))\n",
    "\n",
    "                \n",
    "    if MODEL_SAVED_LOAD:\n",
    "        model_to_save = model.module if hasattr(model, 'module') else model\n",
    "        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)\n",
    "        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)\n",
    "        config = BertConfig(output_config_file)\n",
    "        model = BertForQuestionAnswering(config)\n",
    "        model.load_state_dict(torch.load(output_model_file))\n",
    "        \n",
    "\n",
    "    model.to(device)\n",
    "\n",
    "    if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):\n",
    "        eval_examples = read_squad_examples(\n",
    "            input_file=args.predict_file, is_training=False, version_2_with_negative=args.version_2_with_negative)\n",
    "        eval_features = convert_examples_to_features(\n",
    "            examples=eval_examples,\n",
    "            tokenizer=tokenizer,\n",
    "            max_seq_length=args.max_seq_length,\n",
    "            doc_stride=args.doc_stride,\n",
    "            max_query_length=args.max_query_length,\n",
    "            is_training=False)\n",
    "\n",
    "        logger.info(\"***** Running predictions *****\")\n",
    "        logger.info(\"  Num orig examples = %d\", len(eval_examples))\n",
    "        logger.info(\"  Num split examples = %d\", len(eval_features))\n",
    "        logger.info(\"  Batch size = %d\", args.predict_batch_size)\n",
    "\n",
    "        all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)\n",
    "        all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)\n",
    "        all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)\n",
    "        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)\n",
    "        eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)\n",
    "        # Run prediction for full data\n",
    "        eval_sampler = SequentialSampler(eval_data)\n",
    "        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)\n",
    "\n",
    "        model.eval()\n",
    "        all_results = []\n",
    "        logger.info(\"Start evaluating\")\n",
    "        for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
    "            if len(all_results) % 1000 == 0:\n",
    "                logger.info(\"Processing example: %d\" % (len(all_results)))\n",
    "            input_ids = input_ids.to(device)\n",
    "            input_mask = input_mask.to(device)\n",
    "            segment_ids = segment_ids.to(device)\n",
    "            with torch.no_grad():\n",
    "                batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)\n",
    "                print(batch_start_logits)\n",
    "                print(batch_end_logits)\n",
    "            for i, example_index in enumerate(example_indices):\n",
    "                start_logits = batch_start_logits[i].detach().cpu().tolist()\n",
    "                end_logits = batch_end_logits[i].detach().cpu().tolist()\n",
    "                eval_feature = eval_features[example_index.item()]\n",
    "                unique_id = int(eval_feature.unique_id)\n",
    "                all_results.append(RawResult(unique_id=unique_id,\n",
    "                                             start_logits=start_logits,\n",
    "                                             end_logits=end_logits))\n",
    "                print(\"Printing all result\")\n",
    "                print(all_results)\n",
    "        output_prediction_file = os.path.join(args.output_dir, \"predictions.json\")\n",
    "        output_nbest_file = os.path.join(args.output_dir, \"nbest_predictions.json\")\n",
    "        output_null_log_odds_file = os.path.join(args.output_dir, \"null_odds.json\")\n",
    "        write_predictions(eval_examples, eval_features, all_results,\n",
    "                          args.n_best_size, args.max_answer_length,\n",
    "                          args.do_lower_case, output_prediction_file,\n",
    "                          output_nbest_file, output_null_log_odds_file, args.verbose_logging,\n",
    "                          args.version_2_with_negative, args.null_score_diff_threshold)\n",
    "\n",
    "#if __name__ == \"__main__\":\n",
    "#    main()\n",
    "print (__name__)\n",
    "if __name__ == '__main__':\n",
    "    print ('This program is being run by itself')\n",
    "    main()\n",
    "else:\n",
    "    print ('I am being imported from another module')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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